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LOGIGEN: Logic-Driven Generation of Verifiable Agentic Tasks

arXiv – CS AI|Yucheng Zeng, Weipeng Lu, Linyun Liu, Shupeng Li, Zitian Qu, Chenghao Zhu, Shaofei Li, Zhengdong Tan, Mengyue Liu, Haotian Zhao, Zhe Zhou, Jianmin Wu|March 3, 2026 at 05:00 AM|8 views

🤖AI Summary

Researchers introduce LOGIGEN, a logic-driven framework that synthesizes verifiable training data for autonomous AI agents operating in complex environments. The system uses a triple-agent orchestration approach and achieved a 79.5% success rate on benchmarks, nearly doubling the base model's 40.7% performance.

Key Takeaways

→LOGIGEN addresses data scarcity in training autonomous AI agents through logic-driven synthesis of verifiable training data.
→The framework employs three core components: Hard-Compiled Policy Grounding, Logic-Driven Forward Synthesis, and Deterministic State Verification.
→A Triple-Agent Orchestration system uses Architect, Set Designer, and Explorer agents to generate complex training scenarios.
→The system generated 20,000 complex tasks across 8 domains with guaranteed validity through exact state equivalence checking.
→LOGIGEN-32B achieved 79.5% success rate on τ²-Bench, significantly outperforming the 40.7% baseline model performance.